Condorcet Jury Theorem (1785)

• The Condorcet Jury Theorem states that if each member of a jury has a probability greater than 1/2 of making the correct decision, then as the number of jurors increases, the probability that the majority decision is correct approaches 1.

\[ P(\text{majority correct}) \to 1 \text{ as } n \to \infty \\ \iff \text{independence of errors} \]

Boosting Weak Learners (1988)

Model Stacking (1992)

Ensemble Methods Summary

Stacking into Boosting

• Why not both?

cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)

lin = Pipeline([
  ("scaler", StandardScaler()),
  ("lr", LogisticRegression(max_iter=1000))
])

stack = StackingClassifier(estimators=[("lin", lin)],
  final_estimator=LGBMClassifier(),
  stack_method="predict_proba", passthrough=True, cv=cv
)

stack.fit(X_train, y_train)
y_pred = stack.predict(X_test)

The Dunbar Number (1992)

Dunbar cont’d: How Hedge Funds Manage It

Wisdom of Crowds (2004)

The Common Task Framework (2007-)

Common Task Framework (cont’d)

• “The Kaggle Grandmasters Playbook: 7 Battle-Tested Modeling Techniques for Tabular Data”, September 18, 2025, Nvidia Blog, link.

Machine, Platform, Crowd (2017)

Theory Takeaways

Quant Equity Workflow

• Larkin, Jonathan R., “A Professional Quant Equity Workflow”, Quantopian Blog, 2016, link.
• Separate teams are focused along an assembly line
  • Data acquisition
  • Alpha research (aka feature engineering)
  • Signal combination (aka modeling)
  • Risk and transaction cost modeling
  • Portfolio construction (aka optimization)
  • Execution

Quant Equity Workflow

Quant Equity Workflow

flowchart LR

    DATA(Data) --> UDEF(Universe Definition)

    UDEF --> A1(alpha 1)
    UDEF --> A2(alpha 2)
    UDEF --> ADOTS(alpha...)
    UDEF --> AN(alpha N)

    A1 --> ACOMBO(Alpha Combination)
    A2 --> ACOMBO
    ADOTS --> ACOMBO
    AN --> ACOMBO

    DATA --> TARGET(Target)
    TARGET --> ACOMBO
    TARGET --> PCON
    DATA --> RISK(Risk & T-Cost Models)

    ACOMBO --> PCON(Optimization)
    RISK --> PCON

    PROD{{t-1 Portfolio}} --> PCON
    PCON --> IDEAL{{Ideal Portfolio}}
    IDEAL --> EXEC
    
    EXEC(Execution)

Workflow: Minimal Non-Trivial Implementation

• Craft four simple alphas (momentum, reversal, quality, value)
• Create a target (forward 5d return demeaned)
• Combine alphas with linear model
• Use cvxportfolio machinery for risk model, t-cost model, optimization
• Cvxportfolio repo on github
• Boyd, Stephen, et al. “Multi‑Period Trading via Convex Optimization.” Foundations and Trends in Optimization, vol. 3, no. 1, 2017, pp. 1–76.

Introduction

• Traditional hedge funds: small teams, opaque processes, high fees
  
• New paradigm: decentralized hedge funds
  • Crowdsourced intelligence
    
  • Blockchain-based incentives
    
  • Global participation
    
• Case studies: Numerai, Yiedl, CrowdCent

Numerai – Overview

• San Francisco–based hedge fund (founded 2015)
  
• Crowdsources stock market predictions from global data scientists
  
• Uses a meta-model: ensemble of community models aggregated into live trading strategy
  
• Mission: “solve the hardest problem in finance” with diverse, anonymized datasets

Numerai – Community & Competitions

• Weekly data science tournaments
  
• Participants submit ML predictions on anonymized market data
  
• Scored on correlation with actual returns
  
• Staking with NMR tokens:
  • Stake NMR = confidence in predictions
    
  • Rewards for accuracy, penalties for errors

Numerai – Data & Roles

• Company: curates data, manages fund, constructs stake-weighted meta-model
  
• Community: builds models, submits predictions, stakes tokens
  
• Data: anonymized, obfuscated stock/market datasets
  
• Company executes trades; community generates intelligence

Numerai – Role of Crypto

• Numeraire (NMR): first hedge-fund-issued cryptocurrency
  
• Staking aligns incentives: accurate models gain rewards, inaccurate lose stake
  
• All payouts and stakes occur via Ethereum smart contracts
  
• Supports trustless, global participation

Yiedl – Overview

• Founded 2023, fully DAO-based hedge fund
  
• Mission: replace fund managers with blockchain data science tournaments
  
• Operates via on-chain vaults on Optimism + Synthetix
  
• Managed by token holders, not a centralized GP

Yiedl – Community & Competitions

• Weekly crypto-asset prediction tournaments
  
• Participants forecast asset returns, stake Yiedl tokens
  
• Predictions stored/encrypted on IPFS & blockchain
  
• Smart contracts auto-reward/penalize based on accuracy
  
• Two tracks:
  • Neutral vault: rank assets for market-neutral strategy
    
  • UpDown vault: propose portfolio weights

Yiedl – Data & Roles

• Data: curated decade-long crypto datasets (prices, on-chain metrics, sentiment)
  
• Community: builds predictive models, stakes tokens, governs DAO
  
• Company/DAO: develops infrastructure, aggregates predictions, executes trades via smart contracts
  
• Division: community = what to trade, DAO = how to implement

Yiedl – Role of Crypto

• YIEDL token:
  • Staking in competitions
    
  • Governance (DAO voting)
    
  • Rewards for accurate predictions
    
• All trades executed via DeFi protocols (Synthetix)
  
• On-chain fund = transparent, auditable, permissionless

CrowdCent – Overview

• Hybrid crowdsourced investment platform (2022–2023)
  
• Combines fundamental analysts + data scientists
  
• Mission: democratize asset management with human + machine intelligence
  
• “Next-generation of investing: decentralized, systematized, democratized”

CrowdCent – Community & Competitions

• Analysts: share investment theses (e.g. via SumZero)
  
• Data Scientists: join open ML challenges to evaluate ideas
  
• Examples:
  • Hyperliquid Challenge: rank crypto assets by 10–30d returns
    
  • Equity NLP Challenge: analyze analyst reports for predictive alpha
    
• Leaderboards, percentile scoring, meta-model aggregation

CrowdCent – Data & Roles

• Data:
  • Fundamental research from SumZero (analyst reports, ratings)
    
  • Market + crypto data, engineered features
    
• Community:
  • Analysts generate qualitative ideas
    
  • Data scientists build quantitative models
    
• Company: runs competitions, integrates data, manages portfolio construction & execution

CrowdCent – Role of Crypto

• No native token (as of 2025)
  
• Crypto as an asset class: community builds crypto strategies (Hyperliquid challenge)
  
• Integration with crypto projects:
  • Fund that stakes in Numerai’s NMR ecosystem
    
  • Bridges decentralized funds together
    
• Less “on-chain” than Yiedl, but embraces crypto markets and ethos

Conclusion

• Numerai: pioneered crypto-incentivized crowdsourced hedge fund
  
• Yiedl: DAO-based, on-chain hedge fund built entirely with DeFi
  
• CrowdCent: blends human fundamental research with ML and crypto strategies
  
• Common threads:
  • Community-driven intelligence
    
  • Machine learning aggregation
    
  • Cryptocurrency as incentive + infrastructure
    
• Future hedge funds: open, decentralized, global

Types of Collaboration

• Horizontal
• Vertical

Horizontal

• Human forecasts concatenated with features
• Fit model on both

Horizontal Example, Results

Vertical

• Stepwise: human first, machine second (or vice versa)
• e.g., human generates ideas, machine filters/ranks/optimizes

AI Analyst Adjustment Methodology

inputs:
  w_h[j]      # human start-of-quarter weight for stock j
  g[j]        # DGTW group of stock j
  decile[j]   # predicted decile within g[j] (1=worst ... 10=best)
  ŷ[j]        # predicted DGTW-adjusted return
  NAV         # fund net asset value at start of quarter

initialize:
  w_ai := w_h
  used := ∅    # prevent duplicate use of replacement names

# Keep strong human picks
for j in holdings sorted by descending w_h[j]:
    if decile[j] == 10:
        used.add(j)

Attempt upgrades for others

# Attempt upgrades for others (largest positions first)
for j in holdings sorted by descending w_h[j]:
    if decile[j] in 1..9:
        group := g[j]
        C := { s in group | decile[s] == 10 and s ∉ used }
        if C ≠ ∅:
            # choose best candidate
            k := argmax_s∈C ŷ[s]
            target_value := w_h[j] * NAV
            max_value := 0.20 * market_cap(k)
            delta_value := min(target_value, max_value)
            w_ai[k] +=  delta_value / NAV
            w_ai[j] -=  delta_value / NAV
            used.add(k)

Replace remaining bottom-decile names

# Replace remaining bottom-decile names with the group index
for j in holdings:
    if decile[j] == 1 and w_ai[j] > 0:
        idx := index_for_group(g[j])
        w_ai[idx] += w_ai[j]
        w_ai[j]   = 0

# Normalize / clean
project w_ai onto the simplex (weights ≥ 0, sum = 1)

Vertical Example Results

Thank You!

• Questions? Comments?